Tissue microarray technology offers the opportunity for high throughput analysis of tissue samples (Konen, J. et al., Nat. Med. 4:844–7 (1998); Kallioniemi, O. P. et al., Hum. Mol. Genet. 10:657–62 (2001); Rimm, D. L. et al., Cancer J. 7:24–31 (2001)). For example, the ability to rapidly perform large scale studies using tissue microarrays can provide critical information for identifying and validating drug targets/prognostic markers (e.g. estrogen receptor (ER) and HER2/neu) and candidate therapeutics.
Automated quantitative analysis of tissue samples in microarrays, however, presents several challenges, including heterogeneity of tissue sections, subcellular localization of staining, and the presence of background signals. For example, depending on the type of tumor or tissue section being analyzed, the area of interest may represent nearly the entire sample, or only a small percentage. For instance, a pancreatic carcinoma or lobular carcinoma of the breast with substantial desmoplastic response may show stromal tissue representing a large percentage of the total area. If the goal of the assay is to determine epithelial cell expression of a given marker, a protocol must be used that evaluates only that region. The protocol must not only be able to select the region of interest but also normalize it, so that the expression level read from any given area can be compared with that of other areas. Subcellular localization presents similar challenges. Comparisons of nuclear or membranous staining, for example, are quite different from those in total cytoplasmic staining.
Certain methods (including confocal and convolution/deconvolution microscopy) have been used to quantify expression of proteins at the cellular (or sub-cellular) level within a single high power field (Robinson, J. P. Methods Cell. Biol. 63:89–106 (2001); Shaw, P. Histochem. J. 26:687–94 (1994)). However, these are computationally intensive and laborious techniques, which operate on multiple serial images. As a result, the current standard for analysis of tissue microarrays, like tissue sections, is conventional pathologist-based analysis and grading of the sample according to scale.
Most biomarkers exhibit a parametric (normal, “bell-shaped”) distribution, and consequently are best analyzed by a continuous scale (e.g., 0 to 1000). Unfortunately, manual observation tends to be nominal (e.g. 1+, 2+, 3+), primarily because the human eye in unable to reliably distinguish subtle differences in staining intensity. Several methods have been developed to translate nominal manual observations into a continuous scale. Foremost among these is the H-score where the percent of positively stained cells (0 to 100) is multiplied by the staining intensity (e.g. 0 to 3) to make a theoretically continuous scale (0 to 300). However, the inability to detect subtle differences in staining intensity, particularly at the low and high ends of the scale, as well as the tendency to round scores (e.g. 50% at 3+ for a score of 150, versus 47% at 3+ for a score of 141), limits the effectiveness of the H-score.
Automated systems and methods for rapidly analyzing tissue, including tissue microarrays, that permit the identification and localization of identified biomarkers within tissues and other cell containing samples, are needed.